Summary:
Conditional cash transfers (CCTs), cash transfers targeted to poor households made conditional on investments in children's human capital, have become increasingly popular over the past two decades (Bastagli et al, 2016). However, CCTs have been criticized as some argue that the poorest households may find the conditions too costly to comply with and thus be ...

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Conditional cash transfers (CCTs), cash transfers targeted to poor households made conditional on investments in children's human capital, have become increasingly popular over the past two decades (Bastagli et al, 2016). However, CCTs have been criticized as some argue that the poorest households may find the conditions too costly to comply with and thus be excluded from receiving aid (e.g., Freeland, 2007, Baird et al, 2011). Unconditional cash transfers (UCTs), cash transfers with “no strings attached”, are therefore thought to be superior at alleviating current poverty. Consequently, when deciding whether to impose conditions, governments are thought to trade-off the extent to which they increase human capital investments in children versus the extent to which they alleviate current poverty.

In my job market paper, “The Targeting Benefit of Conditional Cash Transfers” (joint with William Dodds), I argue that conditioning cash transfers on school attendance can improve the targeting of cash transfers to low consumption households, rendering CCTs more effective at alleviating current poverty than UCTs. The central idea is that sending a child to school rather than to work results in a discrete loss of child income, meaning that school enrollment may be negatively correlated with current household consumption. School enrollment can therefore act as an indicator for low consumption households. By conditioning transfers on schooling, governments may be able to target resources towards a group with lower consumption. We refer to this unexplored benefit of CCTs as the targeting benefit.

Highlighting the Targeting Benefit through a simple example

The targeting benefit can best be understood through the following example. Parents value their child's education but sending a child to school means forgoing the income they could earn by working instead. Parents therefore trade-off higher household consumption today with improved future outcomes for their child. All else equal, there will exist a parent with income who is just indifferent between sending their child to school or to work, and parents with income above this cutoff will choose to send their child to school while those below will not. Just above this cutoff, household consumption will discontinuously drop by the amount of the child's potential earnings; this discontinuous drop in household consumption is illustrated in Figure 1 below, where denotes potential child income. Consequently, households that send their child to school may have lower consumption on average. By increasing the share of a program budget to a CCT over a UCT, a government can target transfers towards the households sending their child to school, which may be beneficial if these households have particularly low consumption.

What Determines the Size of the Targeting Benefit?

The paper shows that the size of the targeting benefit will depend on three important quantities: (1) the distribution of parental incomes of eligible households (where prior eligibility into the program is pre-determined) – if the parents sending their children to school are only marginally richer than those not sending their children to school, then the targeting benefit can be large; (2) potential child incomes – the larger the loss of income from sending a child to school, the larger the “lumpiness” of the investment; and (3) the curvature of utility of consumption - understanding curvature is crucial because we need to determine the extent to which the households sending their children to school value receiving an extra dollar today relative to the households not sending their children to school.

While the first two quantities (the parental income distribution and the potential earnings for children) are observable, the third one (curvature of utility) is not. How, then, can governments know whether the targeting benefit could be large or small in their specific context? Following a procedure in the spirit of Chetty (2006), we show that the concavity of utility of consumption can be pinned down from two observable schooling elasticities: i) the income effect of a UCT, which measures how school enrollment changes when we increase the unconditional cash transfer, and ii) the price effect of a CCT, which measures how school enrollment changes when we increase the conditional cash transfer. A large income effect implies that the share of children enrolled in school increases sharply when we increase the value of the UCT. This implies that either (1) households' marginal utility of consumption is diminishing quickly so that the opportunity cost of schooling is decreasing quickly as households get wealthier, or (2) the density of households who are near indifferent to sending their child to school is high. However, if (2) is true, the price effect will also be large. Intuitively, if the ratio of the income effect to the price effect is high, this implies marginal utility is decreasing quickly, so that there is significant curvature in utility of consumption.

Application: Mexico at the time of Progresa

We demonstrate the empirical relevance of the targeting benefit using data from Mexico, home to the large and well-known pioneer CCT program called Progresa. This is a context where the opportunity cost of sending a teenage child to school is high: the data shows that 12-15 year-old children earn around 80% as much as their fathers. What’s more, parents sending their teenage children to school do not earn substantially more than parents not sending their teenage children to school. But how high is the curvature of the utility of consumption? We need to estimate price and income effects to pin this down. The randomization of Progresa grants across villages, combined with detailed panel data from 1997-1999, allows us to identify price effects through a difference-in-difference strategy, noting that the price effect captures the gross change in enrollment from an increase in the CCT. To identify income effects, we use variation in transfers to younger siblings below the age of 12years, as enrollment below the age of 12 is almost 100% (see Figure 2 where we plot enrollment by age for boys and girls separately in 1997, i.e., prior to the grants). This means that transfers to younger siblings can be viewed as unconditional transfers to the household. We find substantial income and price effects, with average income effects around one-third as large as average price effects. These estimates imply a relatively high degree of curvature in the utility of consumption, with an implied coefficient of relative risk aversion of 1.37 (i.e., greater curvature than log utility).

Putting our estimates together, we find that the targeting benefit is relatively large: if the only benefit of imposing conditions to send children to school is improved targeting, 55% of the Progresa budget for secondary school age children should go to a CCT over a UCT.

Policy Implications

UCTs have recently received renewed attention as a tool for poverty alleviation in developing countries (Haushofer and Shapiro, 2016). My paper suggests that this move may be partially unwarranted as CCTs can be superior to UCTs in terms of alleviating current poverty due to the previously overlooked targeting benefit.